Iris identification using contourlet transform
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With the increased emphasis on security and personal authentication, an accurate biometric-based authentication system has become a critical requirement in a variety of applications. Among different biometrics, authentication based on iris features has received a lot of attention since its introduction in 1992. The wavelet transform has been proposed by several researchers for extracting iris features for authentication. Although classical wavelets provide a good performance, they suffer from limited orientation selectivity. In this paper, we investigate the potentials of using the contourlet transform to represent the iris texture. A new iris representation and matching system based on contourlet transform is proposed. The contourlet transform not only shares the multiscale and localization properties of wavelets, but also has a higher degree of directionality and anisotropy. The proposed matching system is experimented in both verification and identification modes. Results have shown the significance of the new technique, especially in case of low quality iris images and highly security demanding applications.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it